Variabilities in Rainfall Onset, Cessation and Length of Rainy Season for the Various Agro-Ecological Zones of Ghana

Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana

Studies on the distribution of lymphatic filariasis (LF) have mostly focused on reporting prevalence at the community level and distribution at the district levels. Understanding the distribution patterns at community levels may help in designing surveillance strategies. This study aimed to characterize the spatial distribution of LF infections in four hotspot communities in Ghana. The research, involving 252 participants, collected demographic data, mass drug administration (MDA) information, household GPS coordinates, and antigen detection test results. The LF prevalence varied significantly among the communities, with Asemda having the highest (33.33%) and Mempeasem having the lowest (4.44%). Females had lower odds of infection than males (OR = 2.67, p = 0.003 CI: 1.39-5.13). Spatial analysis using kernel density, Anselin Local Moran's, Getis-Ord Gi models, Ordinary Least Squares, and Geographic Weighted Regression revealed mixed patterns of spatial autocorrelation. This study identified LF hotspots, indicating clusters of high or low prevalence with some areas showing disparities between MDA coverage and LF positivity rates. Despite these hotspots, the overall distribution of LF appeared random, suggesting the importance of purposeful sampling in surveillance activities. These findings contribute valuable insights into the micro-epidemiology of LF, emphasizing the need for community-specific investigations to understand the factors influencing the effectiveness of MDA programs in controlling filarial infections. The study highlights the importance of refining surveillance strategies based on community-level distribution patterns.

Optimal selection of daily satellite precipitation product based on structural similarity index at 1 km resolution for the Pra catchment, Ghana

Thirteen satellite precipitation products (SPPs), re-gridded to 1 km resolution, were evaluated in terms of the structural similarity index (SSI) over the Pra catchment in Ghana. Three SPP scenarios were considered: Scenario one (S1) was the original SPPs; Scenario two (S2) was bias-corrected SPPs; and Scenario three (S3) was the better of S1 and S2 for each wet day. For each scenario, the best SPP was selected to constitute the 14th SPP referred to as the BEST SPP. Each SPP was evaluated in terms of SSI against the rain gauge rainfield for each wet day. For S1, the top three SPPs were TMPA, GSMAP and CMORPH; for S2, CMORPH, PERCCS and MSWEP were the top three; and for S3, CMORPH, PERCCS and TMPA came out on top in order of decreasing performance. Bias correction led to improvement in the overall SSI measure (SSIM) for 73% of wet days. The BEST SPP increased the SSIM of the best individual SPP by over 50% for S1, and over 30% for both S2 and S3. Comparing the BEST SPP of the three scenarios, S2 increased the SSIM statistic by 20% over that for S1, and SSIM was further improved by 4% for S3. It is highly recommended to use BEST SPP (S3) to generate the required 1 km × 1 km rainfields for the Pra, or other catchments around the world with a sparse rain gauge network, through conditional merging with rain gauge data as demonstrated.

Analysis the characterization of climate change and its impacts on smallholder farmers in Eastern Ethiopia

The impact of climate change on agriculture and food production is manifested through alterations in agro-ecological factors. The region of sub-Saharan Africa is particularly susceptible to the effects of climate change and variability, given its reliance on rainfall-dependent agriculture and inadequate adaptive capabilities. The objective of this study was to analysis of the characterization of climate change and its impacts on smallholder farmers in eastern Ethiopia. Historical daily rainfall, maximum and minimum temperature in 1991-2021 years of 12 weather station were collected from National Meteorological Institute of Ethiopia and NASA/POWER. Data quality control, trend test and outlier detection test were used. Climate characterization (onset, cessation, LGS and Dry/wet spell length) and precipitation concentration index were examined. The earliest, and latest start of rainy season in Kombolcha and Haramaya were 81DOY (March 21) and 225DOY (August 13) within mean 130DOY and 125DOY, respectively. In study area the minimum and maximum length of growing period of was recorded in Chinakesen and Haramaya by the values of 32DOY and 253DOY (Days of the year) respectively. In this arid and semi-arid areas, growing period was recorded under the short season crop maturity while long cropping season maturity does no satisfy crop water requirement due to moisture stress. The coefficient of variation of length of growing season from Mieso and Chinakesen were 36.2% and 47.9% which implies that the areas were highly vulnerable to climate extreme events of drought. Length of growing season (LGS) of Gemechis district was decreasing by a factor -2.533 shows highly significant at alpha value of 0.05 confidence level. Dry spell length probability occurrence of 5 days during the main cropping rainy season is significantly very high. The 30 years of data record suggests that a 29-48% precipitation concentration index of kiremt (JJAS) and belg (FMAM) seasonal rainfall which are demonstrating irregular precipitation. kiremt (JJAS) rainfall trend tests of Babile, Kurfa chale and Mieso districts were increasing by a factor of 2.016, 2.043 and 2.117, respectively and statistically significant at 95% confidence level, in the time period of 1991-2021 years. If furtherstudy will be examined climate extreme indices and adaptation strategies designed to simulate the impacts and variability of climate change in the study area.

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.

Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application

In order to expand the output of solar power systems for efficient integration into the national grid, solar energy resource assessment at site is required. A major impediment however, is the widespread scarcity of radiometric measurements, which can be augmented by satellite observation. This paper assessed the suitability of satellite-based solar radiation resource retrieved from the NASA-POWER archives at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5^\circ \times 0.5^\circ$$\end{document}0.5∘×0.5∘ spatial resolution over Ghana–West Africa, to develop a long-term source reference. The assessment is based on the criteria of comparison with estimations from sunshine duration measurement for 22 synoptic stations. Overall, the satellite-based data compared well with ground-based estimations by r = 0.6–0.94 ± 0.1. Spatiotemporally, the agreement is strongest over the northern half Savannah-type climate during March–May, and weakest over the southern half Forest-type climate during June–August. The assessment provides empirical framework to support solar energy utilization in the sub-region.

Climate change: a friend or foe to food security in Africa?

Extreme climate change is posing an increasing threat to human welfare across countries. Specifically, the devastating floods coupled with the looming spectre of drought are argued to explain cross-country differences in food security. While the debate continues and uncertainties about the precise influence of climate change on food security linger, the question of whether climate change plays a pivotal role in increased hunger and food insecurity across countries remains unanswered. This study presented new evidence of the role of climate change in Africa's food security. We utilised the Mann-Kendall test and Sen's slope estimator to analyse climate change trends. We also employed the pooled mean group technique and the Dumitrescu-Hurlin panel causality test to investigate the effect of climate change on food security in 15 African countries between 1970 and 2016. Our empirical findings revealed three things. First, rainfall plays a decisive role in Africa's food security when examined broadly. However, the significance of the effect of rainfall varied substantially across the 15 countries. Second, we find no robust impact of temperature on food security in the long run. However, the short-run results showed that extreme temperatures impede food security, with varying magnitudes across countries. Third, except for rainfall, a bidirectional causality exists between food security and temperature in Africa. Given the risks associated with rain-fed agriculture, we argue that African countries need to limit their dependence on rain-fed agriculture to boost food production.

Land use/land cover and climate change interaction in the derived savannah region of Nigeria

The interaction of land use/land cover (LULC) and climate change, to a large extent, involves anthropogenic activities. This study was carried out in the derived savannah of Nigeria, a delicate, transition ecological zone between the rainforest and savanna zones where the interaction of LULC and climate change could be well appreciated. Using the remote sensing and GIS, Land Change Modeler (LCM), and multivariate geostatistics tools, the study evaluated coupled interaction between LULC and climate change and assessed the changes in the land use/land cover patterns for the periods 1972, 1986, 2002, and 2019. It also evaluated the present (1941-2019) and future (2020-2050) variability in rainfall patterns and made an attempt to predict the interaction between LULC and climate change in future climate. The results suggest that the urban (built-up) area, waterbody, woodland, and farmland experienced a rapid increase of about 2,400%, 583%, 277%, and 32%, respectively, while the forest cover lost about 39% between 1972 and 2019. Furthermore, the study predicted 46% and 29% reduction in the forested area between 2002 and 2050 and 2019 and 2050, respectively. The study concludes that rainfall will be the major driver of LULC change within the study area under a future climate.

Annual and intra-annual climate variability and change of the Volta Delta, Ghana

Deltas are geographically and socio-ecologically distinct systems, with a unique climate and contextually high vulnerabilities to climate dynamics. Hence, they require specific climate change adaptation and policy responses, informed by delta-scale analysis. However, available climate knowledge on deltas is based mainly on broad-scale analysis that masks information unique to deltas. This applies to the Volta Delta system of Ghana. This study presents annual and intra-annual climate variability and trend analysis carried out across the Volta Delta, using the coefficient of variation (CV), anomaly, Mann-Kendall and Pettit statistics. There were time and space differences in climate change and variability. Statistically significant (P < 0.05) positive trends were observed for the major wet season and the mean annual rainfall for Ada and highly significant (P < 0.01) positive trends for Akatsi. These contrasted with the observations in Adidome, which experienced a statistically highly significant (P < 0.01) decreasing trend in rainfall. There were significant (P< 0.05) increases in annual minimum, maximum, and mean temperatures over time in both coastal and inland delta stations. The annual rate of change of mean temperature ranged from 0.03 to 0.05. Ada, the more coastal location, has experienced a narrow range of temperature change, most probably due to the buffering capacity of the ocean. Point changes were observed in the climate data series in four (4) localities. We recommend that adaption and policy actions should include, the provision of small-scale irrigation, encouraging adoption of drought-resistant crop varieties and crop diversification, and also be made responsive to the existing spatiotemporal climate variability and change within the Volta Delta.

Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits

In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological zones in Ghana were characterized, this time using a crop. The characterization was done based on four farmer-preferred traits (early and late leaf spot disease ratings, and haulm and pod yields) using three models (i.e., AMMI, GGE, and Finlay-Wilkinson regression). These models were used to capture specific levels of phenotype, namely, genotype-by-environment interaction (GE), genotype main effect plus GE (G+GE), and environment and genotype main effects plus GE (E+G+GE), respectively. The effect of three major environmental covariables was also determined using factorial regression. Location main effect was found to be highly significant (p < 0.001), confirming its importance in cultivar placement. However, unlike genotypes where the best is usually adjudged through statistical ranking, locations are judged against a benchmark, particularly when phenotyping for disease severity. It was also found that the locations represent one complex mega-environment, justifying the need to test new technologies, including genotypes in all of them before they can be approved for adoption nationally. Again, depending on the phenotypic level considered, genotypic rankings may change, causing environmental groupings to change. For instance, all locations clustered to form one group in 2017 for early and late leaf spot diseases and pod yield when GE was considered, but the groupings changed when G+GE was considered for the same traits in the same year. As a result, assessing genotypic performance at the various levels to arrive at a consensus decision is suggested. Genotypes ICGV-IS 141120 and ICGV-IS 13937 were found to be the best performing.

Validation of Satellite and Merged Rainfall Data over Ghana, West Africa

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

Assessment of Rainfall Distributions and Characteristics in Coastal Provinces of the Vietnamese Mekong Delta under Climate Change and ENSO Processes

Rainwater is considered a promising alternative water source for coastal areas where freshwater resources are increasingly under pressure. This study evaluates rain regime characteristics that influence the ability to exploit rainwater in the coastal provinces of the Vietnamese Mekong Delta (VMD). In particular, it considers the impact of climate change and El Niño–Southern Oscillation (ENSO) processes. We analyzed rainfall data from 102 monitoring stations across the VMD from 1989 to 2017. Using statistical methods, we explored (1) characteristics of the rainy season, including the dates of onset and cessation and season length; (2) average rainfall volumes; and (3) the maximum number of consecutive nonrainy days during the rainy season and over the year. We also analyzed changes in these characteristics over time, in parallel with ENSO processes and climate change. Trend lines were determined using nonparametric methods, utilizing Sen’s slope estimation and the Mann–Kendall test. Results showed a tendency for the rainy season to start earlier and end later in the western coastal zone of the study area, with season length gradually decreasing towards the East Sea and inland. The shortest rainy season was found in the estuary zone (in the northeast of the VMD). Rainfall was abundant on the west coast, again diminishing gradually towards the East Sea and inland. Rain was also quite plentiful during four dry season observation months in the study area, but it lacked the predictability needed for effective exploitation. The number of consecutive days without rain averaged 96 annually, with a difference of 29 days between the largest and smallest observations. The difference between the provinces with the longest and shortest periods without rain averaged 41 days. Although the impact of climate change on the rain regime is complex, we can say that the rainy season now tends to start earlier, end later, and be lengthier, though without exhibiting clear trends. ENSO processes significantly impacted rainfall regime characteristics, especially the dates of onset and cessation, and season length.

A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box-Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box-Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box-Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe-two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998-2002, 2004-2006, and 2010-2013 and a noticeable periodicity value of 256-512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.

Using climate analogue tools to explore and build smallholder farmer capacity for climate smart agriculture

Background: The phenomenon of climate change (CC) and its attendant challenges in agriculture have been widely document. Climate Smart Agriculture (CSA) focuses on sustainable agriculture intensification for food sovereignty through the adoption of mitigation and adaptation practices. Agriculture provides the livelihood for 70% of rural poor in the developing world, so building farmer capacity in CSA is imperative for food security. Studies show that transformative change must be bottom-up – integrating scientific and ethical dimensions, using participatory research approaches that employ simple comprehensive tools for building participants’ capacity to adapt. Methods: The study uses the “Climate Change Agriculture and Food Security” (CCAFS) climate analogue and weather forecasting tools. These participatory learning tools allow participants to interrogate and explore their own geographical and climatic histories and to draw conclusions on climate variability. This study examined smallholder farmers’ understanding of CC and their resilience to it. The study consisted of 5 stages – selection of tools, planning and training of teams, meetings with community leaders and community members to select participants, focus group discussions, modelling sessions and community dissemination meetings.   Results: Participants showed awareness of CC, explained in terms of rainfall variability, decreasing rainforest, increasing temperature and excessively long hot days.  Farmers illustrated gendered perception of past and present landscapes, time use, past seasonal trends, vulnerabilities and access to key resources. They also observed that natural resources were declining, while population and social infrastructure increased. Participants modelled the shift in seasons and projected possible future scenarios. Finally, participants were willing to adopt climate smart agronomic practices. Conclusions: After establishing that farmers are aware of CC, follow-on-studies addressing the impediments to adaptation and provision of necessary tools and resources to facilitate adaptation must be carried out. This study can also be replicated among a larger smallholder population for increased capacity to practice CSA.